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tensorflow
tfjs-models
TypeScript2018–2025
tensorflow / tfjs-models

tfjs-models

Pretrained models for TensorFlow.js

23Distinct people
25Mentions
0Explicit recommenders
14.8KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Pretrained models for TensorFlow.js

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year25 dated mentions

Observed mentions: 2018-06-14 10:48:01 UTC to 2025-01-15 16:18:59 UTC.

0369122018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 11 mentions; 1 submissions, 10 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 5 mentions; 2 submissions, 3 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC211
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC523
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC11110
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC523
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
Pre-trained TensorFlow.js models This repository hosts a set of pre-trained models that have been ported to TensorFlow.js. The models are hosted on NPM and unpkg so they can be used in any project out of the box. They can be used directly or used in a transfer learning setting with TensorFlow.js. To find out about APIs for models, look at the README in each of the respective directories. In general, we try to hide tensors so the API can be used by non-machine learning experts. For those interested in contributing a model, please file a GitHub issue on tfjs to gauge interest. We are trying to add models that complement the existing set of models and can be used as building blocks in other apps. Models Type Model Demo Details Install Images MobileNet live Classify images with labels from the ImageNet database. npm i @tensorflow-models/mobilenet source Hand live Real-time hand pose detection in the browser using TensorFlow.js. npm i @tensorflow-models/hand-pose-detection source Pose live An API for real-time human pose detection in the browser. npm i @tensorflow-models/pose-detection source Coco SSD Object detection model that aims to localize and identify multiple objects in a single image. Based on the TensorFlow object detection API. npm i @tensorflow-models/coco-ssd source DeepLab v3 Semantic segmentation npm i @tensorflow-models/deeplab source Face Landmark Detection live Real-time 3D facial landmarks detection to infer the approximate surface geometry of a human face npm i @tensorflow-models/face-landmarks-detection source Audio Speech Commands live Classify 1 second audio snippets from the speech commands dataset. npm i @tensorflow-models/speech-commands source Text Universal Sentence Encoder Encode text into a 512-dimensional embedding to be used as inputs to natural language processing tasks such as sentiment classification and textual similarity. npm i @tensorflow-models/universal-sentence-encoder source Text Toxicity live Score the perceived impact a comment might have on a conversation, from "Very toxic" to "Very healthy". npm i @tensorflow-models/toxicity source Depth Estimation Portrait Depth live Estimate per-pixel depth (the distance to the camera center) for a single portrait image, which can be further used for creative applications such as 3D photo and relighting. npm i @tensorflow-models/depth-estimation source General Utilities KNN Classifier This package provides a utility for creating a classifier using the K-Nearest Neighbors algorithm. Can be used for transfer learning. npm i @tensorflow-models/knn-classifier source Development You can run the unit tests for any of the models by running the following inside a directory: yarn test New models should have a test NPM script (see this package.json and run_tests.ts helper for reference). To run all of the tests, you can run the following command from the root of this repo: yarn presubmit
Read the full README on GitHub

What Hacker News says

25 sources
So there are models for Pose estimation like [PoseNet] or Qualcomm's [MediaPipe] that give you a graph of the body position. This metadata can be converted to a string representation and be fed into a generative model. If I record myself in different exercises, extract the poses, and label them with the appropriate steps, I might be able to perform few-shot inference or RAG over a set of labeled positions, such as the model can infer new body positions from exercise steps. If that'd possible, then it is fairly easy:tm: to generate illustrations (e.g. like stickman figures. See [Kawamoto] for an example) Of course, none of this is necessary if I can record (or let users record) and upload videos of each exercise, having enough of them to not feel they are repetitive. [PoseNet](https://github.com/tensorflow/tfjs-models/tree/master/posenet
Read the rest
In Show HN: Move Frequently – Stay active with a 1-min exercise every 45 minutes
Heck yeah! Love the touches like resize handling or rotation support! Contributions are more than welcome... hint hint ;) > It uses face-api.js to find the face Yeah, I'm using Google AI's Face Detector [1]. There's Tensorflow's Face Landmarks Detection [2] that looked most promising and accurate. But it had two bugs [3][4] that are blockers. The first one got fixed recently, but the other one is still pending. [1] https://ai.google.dev/edge/mediapipe/solutions/vision/face_d... [2] https://github.com/tensorflow/tfjs-models/tree/master/face-l... [3] https://github.com/tensorflow/tfjs/issues/7905 [4] https://github.com/tensorflow/tfjs/issues/8290
In Show HN: I built the most over-engineered Deal With It emoji generator
For anyone wondering it is Electron app, that packs some heavy web app assets(300mb + 150mb runtime), but still loads rest of web app from https://client.getposturehealth.co, and then it uses google pretrained posenet tensorflowjs model[0][1]. (loads it from https://storage.googleapis.com/tfjs-models/savedmodel/posene...). In perfect world one would made it offline, release it at fixed price and be done with it, in our imperfect world it is turned in infinite cash-cow that requires connectivity and bazillion metrics. [0] https://medium.com/tensorflow/real-time-human-pose-estimatio... [1] https://github.com/tensorflow/tfjs-models/blob/master/posene...
In Launch HN: Zen (YC S21) – Posture-correcting software via webcam
This was a fun little weekend project I made by smooshing together a few libraries. I was surprised by how fast and accurate the pose estimation library [0] was, especially running real-time in the browser! This project requires some extensive setup and special hardware like a projector, so I expect most people won't be able to try it on their own -- which is a bummer, because it's really fun to play! But there are videos linked in the readme, and I'll link them again here [1][2] [0] https://github.com/tensorflow/tfjs-models/tree/master/posene... [1] https://www.youtube.com/watch?v=T-GrpBywY18 demo [2] https://www.youtube.com/watch?v=AVGCd5r4ihw calibration sequence
In Show HN: Play the Chrome T-Rex game by physically jumping

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